Particle Swarm Optimization Algorithm Based Artificial Neural Network for Botnet Detection

Panimalar, P.

doi:10.1007/s11277-021-08841-1

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…e model has the ability of parallel distributed processing, high fault tolerance, and selflearning ability. It combines the acquired information through processing and storage and has attracted the attention of people in all walks of life with its unique knowledge expression and adaptive learning ability [19]. e basic unit of artificial network is neuron, and the neuron model structure includes input part, output part, and calculation function part [20].…”

Section: Methodsmentioning

confidence: 99%

Optimization of TCM Diagnosis Information Management System Based on Artificial Neural Networks

Jia

2022

Mathematical Problems in Engineering

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As a treasure of Chinese medicine, TCM has gradually formed and developed into a complete medicine with a unique medical theory system and rich treatment experience after thousands of years of medical practice. It requires high diagnostic experience, which is not conducive to application promotion and management. Therefore, the concept of digital medicine has been recognized by more and more people, in which medical diagnosis is one of the core issues of digital medicine. The accuracy and efficiency of medical diagnosis are closely related to people’s life and health, which is an important problem that cannot be ignored. Use the growing case base as knowledge base to reason and realize the diagnosis function of traditional Chinese medicine. Based on the characteristics of traditional Chinese medicine and taking case reasoning as a model, an expert system of traditional Chinese medicine diagnosis is established. This paper combines the strong learning ability, strong adaptability, and large-scale parallel processing ability of artificial neural networks (ANN) to solve the nonlinear and unstructured complex problems in management information system. By improving BP algorithm to optimize the error of weight and repair or energy parameters, the overall error of the optimized system is reduced by about 75.3% after experimental analysis, and the average accuracy of prediction is 75%.

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Section: Methodsmentioning

confidence: 99%

Optimization of TCM Diagnosis Information Management System Based on Artificial Neural Networks

Jia

2022

Mathematical Problems in Engineering

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“…In 2021, high accuracy and precision were achieved with ML classifiers using datasets collected via honeypots [13]. Moreover, in 2021, Artificial Neural Network Particle Swarm Optimization (ANN PSO) was used to apply ML and swarm optimization to botnet detection with high accuracy [14]. Despite the high accuracy and precision in botnet detection achieved in these works, there are limitations.…”

Section: Cybersecurity Measuresmentioning

confidence: 99%

Security Awareness in Smart Homes and Internet of Things Networks through Swarm-Based Cybersecurity Penetration Testing

Schiller,

Caulkins,

et al. 2023

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Internet of Things (IoT) devices are common in today’s computer networks. These devices can be computationally powerful, yet prone to cybersecurity exploitation. To remedy these growing security weaknesses, this work proposes a new artificial intelligence method that makes these IoT networks safer through the use of autonomous, swarm-based cybersecurity penetration testing. In this work, the introduced Particle Swarm Optimization (PSO) penetration testing technique is compared against traditional linear and queue-based approaches to find vulnerabilities in smart homes and IoT networks. To evaluate the effectiveness of the PSO approach, a network simulator is used to simulate smart home networks of two scales: a small, home network and a large, commercial-sized network. These experiments demonstrate that the swarm-based algorithms detect vulnerabilities significantly faster than the linear algorithms. The presented findings support the case that autonomous and swarm-based penetration testing in a network could be used to render more secure IoT networks in the future. This approach can affect private households with smart home networks, settings within the Industrial Internet of Things (IIoT), and military environments.

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Intelligent Traffic Flow Prediction Model Based on Deep Neural Network

2023

Lecture Notes on Data Engineering and Communications Technologies

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Particle Swarm Optimization Algorithm Based Artificial Neural Network for Botnet Detection

Cited by 4 publications

References 16 publications

Optimization of TCM Diagnosis Information Management System Based on Artificial Neural Networks

Optimization of TCM Diagnosis Information Management System Based on Artificial Neural Networks

Security Awareness in Smart Homes and Internet of Things Networks through Swarm-Based Cybersecurity Penetration Testing

Intelligent Traffic Flow Prediction Model Based on Deep Neural Network

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